如何使用文本数据调整随机森林分类器的超参数

Question

我想使用CV方法来调整随机森林分类器的超参数。 在这一点上，我很乐意简单地调整理想的树木数量（n_estimators）。

我的输入变量是一个文本字符串（独立var）和一个标签（dep var）。 我感到困惑的是，TfidfVectorizer在哪里发挥作用？

我到处搜索示例代码，但是还没有发现任何有用的东西。

from sklearn.model_selection import cross_validate

pipeline_cv = Pipeline([
    ('bow', TfidfVectorizer(analyzer=text_process)),  
    ('tfidf', TfidfTransformer()),  
    ('classifier', RandomForestClassifier()),  
])

parameters = {
    'n_estimators'      : [10,50,200],
    'random_state'      : [108],
    'min_samples_leaf'  : [2,3,5],
    'min_samples_split' : [2,3,5] 
}

clf = cross_validate(pipeline_cv, parameters)
clf.fit(text_train, value_train)

我收到的错误如下。 我不确定是什么触发了KeyError。

---------------------------------------------------------------------------
KeyError                                  Traceback (most recent call last)
<ipython-input-367-e5e6e92c059d> in <module>
     14 }
     15 
---> 16 clf = cross_validate(pipeline_cv, parameters)
     17 clf.fit(text_train, value_train)

~\AppData\Local\Continuum\anaconda3\lib\site-packages\sklearn\model_selection\_validation.py in cross_validate(estimator, X, y, groups, scoring, cv, n_jobs, verbose, fit_params, pre_dispatch, return_train_score, return_estimator, error_score)
    229             return_times=True, return_estimator=return_estimator,
    230             error_score=error_score)
--> 231         for train, test in cv.split(X, y, groups))
    232 
    233     zipped_scores = list(zip(*scores))

~\AppData\Local\Continuum\anaconda3\lib\site-packages\joblib\parallel.py in __call__(self, iterable)
    919             # remaining jobs.
    920             self._iterating = False
--> 921             if self.dispatch_one_batch(iterator):
    922                 self._iterating = self._original_iterator is not None
    923 

~\AppData\Local\Continuum\anaconda3\lib\site-packages\joblib\parallel.py in dispatch_one_batch(self, iterator)
    757                 return False
    758             else:
--> 759                 self._dispatch(tasks)
    760                 return True
    761 

~\AppData\Local\Continuum\anaconda3\lib\site-packages\joblib\parallel.py in _dispatch(self, batch)
    714         with self._lock:
    715             job_idx = len(self._jobs)
--> 716             job = self._backend.apply_async(batch, callback=cb)
    717             # A job can complete so quickly than its callback is
    718             # called before we get here, causing self._jobs to

~\AppData\Local\Continuum\anaconda3\lib\site-packages\joblib\_parallel_backends.py in apply_async(self, func, callback)
    180     def apply_async(self, func, callback=None):
    181         """Schedule a func to be run"""
--> 182         result = ImmediateResult(func)
    183         if callback:
    184             callback(result)

~\AppData\Local\Continuum\anaconda3\lib\site-packages\joblib\_parallel_backends.py in __init__(self, batch)
    547         # Don't delay the application, to avoid keeping the input
    548         # arguments in memory
--> 549         self.results = batch()
    550 
    551     def get(self):

~\AppData\Local\Continuum\anaconda3\lib\site-packages\joblib\parallel.py in __call__(self)
    223         with parallel_backend(self._backend, n_jobs=self._n_jobs):
    224             return [func(*args, **kwargs)
--> 225                     for func, args, kwargs in self.items]
    226 
    227     def __len__(self):

~\AppData\Local\Continuum\anaconda3\lib\site-packages\joblib\parallel.py in <listcomp>(.0)
    223         with parallel_backend(self._backend, n_jobs=self._n_jobs):
    224             return [func(*args, **kwargs)
--> 225                     for func, args, kwargs in self.items]
    226 
    227     def __len__(self):

~\AppData\Local\Continuum\anaconda3\lib\site-packages\sklearn\model_selection\_validation.py in _fit_and_score(estimator, X, y, scorer, train, test, verbose, parameters, fit_params, return_train_score, return_parameters, return_n_test_samples, return_times, return_estimator, error_score)
    503     start_time = time.time()
    504 
--> 505     X_train, y_train = _safe_split(estimator, X, y, train)
    506     X_test, y_test = _safe_split(estimator, X, y, test, train)
    507 

~\AppData\Local\Continuum\anaconda3\lib\site-packages\sklearn\utils\metaestimators.py in _safe_split(estimator, X, y, indices, train_indices)
    199             X_subset = X[np.ix_(indices, train_indices)]
    200     else:
--> 201         X_subset = safe_indexing(X, indices)
    202 
    203     if y is not None:

~\AppData\Local\Continuum\anaconda3\lib\site-packages\sklearn\utils\__init__.py in safe_indexing(X, indices)
    221             return X[indices]
    222     else:
--> 223         return [X[idx] for idx in indices]
    224 
    225 

~\AppData\Local\Continuum\anaconda3\lib\site-packages\sklearn\utils\__init__.py in <listcomp>(.0)
    221             return X[indices]
    222     else:
--> 223         return [X[idx] for idx in indices]
    224 
    225 

KeyError: 2